Native Mass Spectrometry Quantitation of α2-3-Linked <i>N</i>-Acetylneuraminic Acid Content of Prostate-Specific Antigen: An Accurate Liquid Biopsy for Clinically Significant Prostate Cancer
Bibliographic record
Abstract
Application of the prostate-specific antigen (PSA) test, which measures PSA levels in blood, is standard in prostate cancer (PCa) screening. However, because PSA levels may be elevated for reasons other than PCa, it leads to high rates of misdiagnosis and overtreatment. Recently, alteration in the N -glycan sialylation of PSA, specifically increased levels of α2-3-linked N -acetylneuraminic acid (α2-3-Neu5Ac or α2-3-sialic acid), was identified as a potential biomarker for clinically significant PCa. Here, we introduce a robust top-down native mass spectrometry (MS) approach, performed using a combination of α2-3-Neu5Ac-specific and nonspecific neuraminidases and employing center-of-mass monitoring (CoMMon), for quantifying the levels of α2-3-Neu5Ac as a fraction of total N -linked Neu5Ac present on PSA extracted from blood serum. To illustrate the potential of the assay for clinical diagnosis and disease staging of PCa, the percentages of α2-3-Neu5Ac on PSA (%α23PSA) in the serum of low-grade (International Society of Urological Pathology Grade Group/GG1), intermediate-grade (GG2), and high-grade (GG3,4,5) PCa individuals were measured. We observed a high sensitivity (85.5%) and specificity (84.6%) for discrimination of GG1 from clinically significant GG2–5 patients when using a %α23PSA test cut-off of 28.0%. Our results establish that the %α23PSA in blood serum PSA, which can be precisely measured in a non-invasive manner with our dual neuraminidase native MS/CoMMon assay, can discriminate between clinically significant PCa (GG2–5) and low-grade PCa (GG1). Such discrimination has not been previously achieved and represents an important clinical need. This assay could greatly improve the standard PSA test and serve as a valuable PCa diagnostic tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".